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So we have computed directed asynchrony measure, which transforms the asynchrony observed for when the correct L1 and L2 stress are differently placed, such that negative asynchronies indicate an asynchrony towards L2 and positive values indicate asynchrony towards L1 direction. So if a there very positive directed asynchronies, it seems that gesture is more attracted towards L1 relative to speech. This measure is especially informative for the mismatch trials, as for matched trials the asynchrony is not transformed.

a <- ggplot(D[D$condition == "gesture", ], aes(x= asynchrony, color = ppn)) + geom_density()+theme_bw()+facet_grid(.~accent)
a <- a + scale_color_manual(values=wes_palette(n=2, name = "Darjeeling1"))
ggplotly(a)
a <- ggplot(D[D$condition == "gesture", ], aes(x = stress, y= asynchrony_L1L2, color=ppn)) +geom_boxplot(alpha = 0) + geom_quasirandom()+theme_bw()+ coord_flip()
a <- a + scale_color_manual(values=wes_palette(n=2, name = "Darjeeling1"))
ggplotly(a)
a <- ggplot(D[D$stressed_mistimingL1L2!=0,], aes(x= stressed_mistimingL1L2, color = condition)) + geom_density()+theme_bw()
a <- a + scale_color_manual(values=wes_palette(n=2, name = "BottleRocket2")) + facet_grid(stress~accent)
ggplotly(a)

Power analysis

https://link.springer.com/article/10.3758/s13428-021-01546-0

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